A reinforcement learning approach to web API recommendation for mashup development

Richard Anarfi; Kenneth K. Fletcher

doi:10.1109/SERVICES.2019.00109

A reinforcement learning approach to web API recommendation for mashup development

Richard Anarfi
, Kenneth K. Fletcher

Computer Science

University of Massachusetts Boston

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents an approach to web API recommendation for mashup development using reinforcement learning (RL). Specifically, we present a RL approach, capable of adapting to the dynamic nature of web API quality properties to recommend web APIs for optimal mashup solution. The approach is also capable of recommending replacement web APIs to existing mashups in a dynamic environment, where the quality properties of the component web APIs continue to change. Since it is challenging to obtain quality of service parameters, our approach models mashup reward using external quality factors of web APIs, which drives the evaluation of its suitability for integration into a mashup application.

Original language	English
Title of host publication	Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019
Editors	Carl K. Chang, Peter Chen, Michael Goul, Katsunori Oyama, Stephan Reiff-Marganiec, Yanchun Sun, Shangguang Wang, Zhongjie Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	372-373
Number of pages	2
ISBN (Electronic)	9781728138510
DOIs	https://doi.org/10.1109/SERVICES.2019.00109
State	Published - Jul 2019
Event	2019 IEEE World Congress on Services, SERVICES 2019 - Milan, Italy Duration: Jul 8 2019 → Jul 13 2019

Publication series

Name	Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019

Conference

Conference	2019 IEEE World Congress on Services, SERVICES 2019
Country/Territory	Italy
City	Milan
Period	7/8/19 → 7/13/19

ASJC Scopus Subject Areas

Hardware and Architecture
Information Systems and Management
Safety, Risk, Reliability and Quality
Management Science and Operations Research
Artificial Intelligence
Computer Networks and Communications
Information Systems

Keywords

Mashup Development
Reinforcement Learning
Web API Quality
Web API Recommendation

Access to Document

10.1109/SERVICES.2019.00109

Cite this

Anarfi, R., & Fletcher, K. K. (2019). A reinforcement learning approach to web API recommendation for mashup development. In C. K. Chang, P. Chen, M. Goul, K. Oyama, S. Reiff-Marganiec, Y. Sun, S. Wang, & Z. Wang (Eds.), Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019 (pp. 372-373). Article 8817173 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SERVICES.2019.00109

A reinforcement learning approach to web API recommendation for mashup development. / Anarfi, Richard; Fletcher, Kenneth K.
Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. ed. / Carl K. Chang; Peter Chen; Michael Goul; Katsunori Oyama; Stephan Reiff-Marganiec; Yanchun Sun; Shangguang Wang; Zhongjie Wang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 372-373 8817173 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Anarfi, R & Fletcher, KK 2019, A reinforcement learning approach to web API recommendation for mashup development. in CK Chang, P Chen, M Goul, K Oyama, S Reiff-Marganiec, Y Sun, S Wang & Z Wang (eds), Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019., 8817173, Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019, Institute of Electrical and Electronics Engineers Inc., pp. 372-373, 2019 IEEE World Congress on Services, SERVICES 2019, Milan, Italy, 7/8/19. https://doi.org/10.1109/SERVICES.2019.00109

Anarfi R, Fletcher KK. A reinforcement learning approach to web API recommendation for mashup development. In Chang CK, Chen P, Goul M, Oyama K, Reiff-Marganiec S, Sun Y, Wang S, Wang Z, editors, Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 372-373. 8817173. (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019). doi: 10.1109/SERVICES.2019.00109

Anarfi, Richard ; Fletcher, Kenneth K. / A reinforcement learning approach to web API recommendation for mashup development. Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. editor / Carl K. Chang ; Peter Chen ; Michael Goul ; Katsunori Oyama ; Stephan Reiff-Marganiec ; Yanchun Sun ; Shangguang Wang ; Zhongjie Wang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 372-373 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019).

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title = "A reinforcement learning approach to web API recommendation for mashup development",

abstract = "This paper presents an approach to web API recommendation for mashup development using reinforcement learning (RL). Specifically, we present a RL approach, capable of adapting to the dynamic nature of web API quality properties to recommend web APIs for optimal mashup solution. The approach is also capable of recommending replacement web APIs to existing mashups in a dynamic environment, where the quality properties of the component web APIs continue to change. Since it is challenging to obtain quality of service parameters, our approach models mashup reward using external quality factors of web APIs, which drives the evaluation of its suitability for integration into a mashup application.",

keywords = "Mashup Development, Reinforcement Learning, Web API Quality, Web API Recommendation",

author = "Richard Anarfi and Fletcher, \{Kenneth K.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE World Congress on Services, SERVICES 2019 ; Conference date: 08-07-2019 Through 13-07-2019",

year = "2019",

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doi = "10.1109/SERVICES.2019.00109",

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series = "Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019",

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AB - This paper presents an approach to web API recommendation for mashup development using reinforcement learning (RL). Specifically, we present a RL approach, capable of adapting to the dynamic nature of web API quality properties to recommend web APIs for optimal mashup solution. The approach is also capable of recommending replacement web APIs to existing mashups in a dynamic environment, where the quality properties of the component web APIs continue to change. Since it is challenging to obtain quality of service parameters, our approach models mashup reward using external quality factors of web APIs, which drives the evaluation of its suitability for integration into a mashup application.

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A reinforcement learning approach to web API recommendation for mashup development

Abstract

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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